Knowledge catalogue
arXiv:2609.31704v1 Announce Type: cross Abstract: Large language models are increasingly used as interacting agents, but it remains unclear how robust their coordination is when public communication i
arXiv:2609.32269v1 Announce Type: cross Abstract: Agents that answer questions from compressed or retrieved memory must recognize when the evidence a query needs is no longer in memory. Benchmarks for
arXiv:2609.34785v1 Announce Type: cross Abstract: Developing agents for hardware design and verification requires reliable correctness feedback. As a hardware specification may permit correct implemen
arXiv:2609.35615v1 Announce Type: new Abstract: Behavioral Foundation Models (BFMs) are an emerging paradigm in reinforcement learning, playing a role analogous to large language models in natural lan
arXiv:2609.33940v1 Announce Type: new Abstract: Detecting failures in World Model (WM)-based planning requires monitoring whether the model is behaviorally aligned with the current task, which in turn
arXiv:2609.32743v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models (FMs) promise transferable neural representations, yet their advantages over strong supervised baselines
arXiv:2609.33675v1 Announce Type: new Abstract: Understanding why predictors can generalize despite interpolating noisy training data is a central puzzle in machine learning. Most work on such 'benign
arXiv:2609.33254v1 Announce Type: new Abstract: Understanding how drugs interact with protein targets is fundamental to drug discovery, drug repurposing and the early identification of promising thera
arXiv:2609.33209v1 Announce Type: cross Abstract: Typed-decision models such as TypeSafe's Jev answer a declared yes/no or multiple-choice question about a state with a probability instead of text, an
arXiv:2609.33336v1 Announce Type: new Abstract: Model-based imitation learning (MBIL) improves real-environment interaction efficiency by optimizing policies on imagined rollouts from a learned world
arXiv:2609.34098v1 Announce Type: new Abstract: Semantic knowledge is increasingly used to bridge heterogeneous schemas in tabular learning, but how much does that knowledge actually improve predictio
arXiv:2609.35569v1 Announce Type: cross Abstract: Large language model (LLM) serving has environmental impacts across energy consumption, carbon emission, water consumption, and biodiversity loss. Yet
arXiv:2609.33764v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) perform well on homophilic graphs but struggle in heterophilic settings, where connected nodes often carry dissimilar label
arXiv:2609.32774v1 Announce Type: new Abstract: Effective-connectivity estimation from brain signals often relies on Gaussian residual modeling, which enables tractable estimation but can discard info
arXiv:2609.34143v1 Announce Type: new Abstract: Existing 3D industrial anomaly detection mainly targets local geometric deviations. In contrast, many industrial anomalies violate object-level design o
arXiv:2609.35060v1 Announce Type: new Abstract: Inferring dynamics from snapshots of evolving distributions is fundamentally underdetermined: the Fokker-Planck equation constrains the drift F only thr
arXiv:2601.10214v3 Announce Type: replace Abstract: Camera control has been extensively studied in conditioned video generation; however, performing precisely altering the camera trajectories while fa
arXiv:2609.31753v1 Announce Type: cross Abstract: Video anomaly detection for patrol robots and surveillance systems must recognize abnormal interactions among familiar entities. Existing pixel-recons
arXiv:2609.31777v1 Announce Type: cross Abstract: Magnetic resonance imaging involves an inherent trade-off among spatial resolution, acquisition time, and noise. This trade-off contributes to long sc
arXiv:2609.34083v1 Announce Type: new Abstract: Recommendation models with sparse embeddings and a shared consumer often exhibit the one-epoch phenomenon: a second epoch lowers training loss while sha
arXiv:2609.33595v1 Announce Type: cross Abstract: Joint-embedding world models enable visual planning by learning action-conditioned dynamics in latent space. Yet they are commonly trained for one-ste
arXiv:2609.34765v1 Announce Type: new Abstract: Post-training quantization (PTQ) enables efficient deployment of large vision-language models (LVLMs), but is typically calibrated on a small set while
arXiv:2609.34145v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) gives vision--language driving systems access to external safety knowledge, yet a retrieved risk rule may be releva
arXiv:2609.35294v1 Announce Type: new Abstract: Object hallucination remains a major challenge for large vision-language models. While off-policy preference optimization proves to be an effective solu
arXiv:2609.35232v1 Announce Type: new Abstract: Visual-token compression is effective for improving the efficiency of vision-language models, but under extreme compression budgets, token pruning can b
arXiv:2609.34611v1 Announce Type: new Abstract: Cross-site out-of-distribution (OOD) generalization in resting-state functional magnetic resonance imaging (rs-fMRI) often relies on learning task-discr
arXiv:2609.31812v1 Announce Type: cross Abstract: Pruning reduces the number of weights in magnetic resonance imaging (MRI) reconstruction networks. Equal sparsity, however, can retain weights with di
arXiv:2609.33145v1 Announce Type: new Abstract: Constructing action targets from measured robot motion is an established approach in imitation learning. Under interaction constraints, however, command
arXiv:2609.33165v1 Announce Type: new Abstract: Recent advances in robot learning have produced increasingly capable embodied agents. Yet comparatively less attention has been given to a more basic fo
arXiv:2609.35347v1 Announce Type: new Abstract: Reinforcement learning can turn one language model into several specialists, each excellent at a single skill such as mathematics, coding or following i
arXiv:2609.32841v1 Announce Type: new Abstract: One-step text-guided diffusion editing is efficient but prone to spatially misallocated updates that distort the edited object and alter the background.
arXiv:2609.31667v1 Announce Type: new Abstract: Accurate bus Estimated Time of Arrival (ETA) prediction is vital for urban mobility, passenger satisfaction, and transit efficiency, yet existing models
arXiv:2609.32432v1 Announce Type: new Abstract: Flow matching and diffusion can be trained to predict different quantities, most commonly the data x_1, the source noise x_0, or the velocity~v. Althoug
arXiv:2609.33144v1 Announce Type: new Abstract: Looped Transformers can generalize to reasoning chains longer than those encountered during training, but the computations enabling this behavior and li
arXiv:2609.33391v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) often relies on sparse outcome rewards, providing coarse supervision for long-horizon agents. On-p
arXiv:2609.32961v1 Announce Type: new Abstract: As LLM agents tackle longer tasks, they increasingly compress growing histories of reasoning, actions, and tool outputs. Compression can reduce token us
arXiv:2609.35521v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) expose features that help us understand and steer language models, but faithful reconstruction does not guarantee informati
arXiv:2609.34857v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains reasoning models to produce correct answers, but does not ensure that their stated confiden
arXiv:2609.31740v1 Announce Type: new Abstract: Accurate coronary artery segmentation on coronary computed tomography angiography (CCTA) is essential for diagnosing coronary artery disease. Deep netwo
arXiv:2609.32720v1 Announce Type: new Abstract: Reward models score responses from large language models (LLMs) and guide LLM training toward human preferences. However, reward models can favor superf
arXiv:2505.11294v3 Announce Type: replace Abstract: Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing different components to address each part, making them
arXiv:2512.22500v4 Announce Type: cross Abstract: Modern nuclear data evaluation increasingly requires not only accurate scattering calculations, but also efficient methods for uncertainty quantificat
arXiv:2609.35407v1 Announce Type: new Abstract: Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-
arXiv:2609.32180v1 Announce Type: new Abstract: Audio-visual segmentation (AVS) aims to segment sounding objects at the pixel level by integrating auditory and visual cues. However, existing methods a
arXiv:2609.33800v1 Announce Type: new Abstract: Multimodal learning beyond two modalities commonly leverages a specific modality (e.g., text) to bind other modalities. However, how to establish a more
arXiv:2609.32467v1 Announce Type: new Abstract: Predicting transcriptional responses to unseen compounds is limited by fragmented chemical coverage and heterogeneous experimental platforms and gene pa
arXiv:2609.33303v1 Announce Type: new Abstract: Despite recent progress in irregular time series forecasting, the field still lacks a unified benchmark for fair and comprehensive evaluation. Existing
arXiv:2609.32361v1 Announce Type: new Abstract: Debate distillation adapts weaker verifiers using multi-agent debate transcripts to improve their judgement in subsequent debates, but gains on monitore
arXiv:2609.34622v1 Announce Type: new Abstract: Poisson denoising of scientific data requires methods that account for signal-dependent noise while accommodating different data dimensionalities and pr
arXiv:2607.10427v2 Announce Type: replace Abstract: Local motion blur detection requires pixel-level localization of blurred regions. Existing benchmarks let models rely on gradient shortcuts that fai
arXiv:2609.33722v1 Announce Type: new Abstract: Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typica
arXiv:2609.35635v1 Announce Type: new Abstract: In materials machine learning, closely related retained structures can sustain accurate property predictions even after removing a specific record, rend
arXiv:2506.07325v2 Announce Type: replace Abstract: Model Predictive Path Integral (MPPI) control provides a sampling-based framework for optimal control, while Control Barrier Functions (CBFs) provid
arXiv:2609.34561v1 Announce Type: new Abstract: Motor brain-computer interfaces (BCIs) aim to decode motor intention, enabling people with paralysis to control external devices. Neural motor decoding
BREAKING: Elon Musk was sitting next to President Trump at the White House Super Intelligence luncheon. Here is President Trump’s full address: • Praised the technology leaders as some of the world’s
BREAKING: Grok is helping power America .gov, the U.S. government’s new AI website. 🇺🇸 U.S. Chief Design Officer Joe Gebbia says Grok & Gemini power the site. Americans can ask questions and get answe
🚨BREAKING: “Months before OpenAI's artificial intelligence went rogue, two employees raised an alarm with top executives. They were ignored….OpenAI executives told the employees that the tests needed
BREAKING: OpenAI just APOLOGIZED and ADMITTED their AI agent breached not one, but FOUR Australian government departments 'We are sorry and working to do better in the future.' The breached agencies:
BREAKING: OpenAI postpones 6.1 for safety reasons. If we can take OpenAI at face value, this is quite a decision—and also a sign that no magical solution is in immediate view. Per @sheeraf at NYT “GPT
arXiv:2511.12257v2 Announce Type: replace-cross Abstract: This paper proposes a novel Bayesian framework for solving Poisson inverse problems by devising a Monte Carlo sampling algorithm which account